How is AI Changing the Higher Education Experience?

Artificial Intelligence (AI) is transforming education at an unprecedented pace, yet its role in higher education remains a subject of debate. How do educators make sense of AI’s impact on teaching, learning, and assessment? To explore this, we conducted a study with educators and students, uncovering insights into how they are using AI, their concerns, and how they are navigating the complexities of AI integration. Participants provided valuable insights into their AI usage, attitudes, and institutional challenges, offering a nuanced view of AI’s evolving role in academia.

In December 2024, we conducted three online focus groups with 62 educators, followed by a student focus group in January 2025 with 6 participants. Discussions were recorded and analysed through thematic analysis, identifying key patterns in AI adoption, concerns, and institutional challenges. The educator sessions were facilitated by the research team, while the student focus group was led by a student campus partner (a scheme which promotes paid internships that are available across the university’s campuses) to create a comfortable environment for open discussion. We included both students and educators to capture diverse perspectives on AI in higher education, examining how it impacts teaching, learning, and assessment from both educator and learner viewpoints.

We explored how educators are making sense of AI’s presence in higher education. We found that educators are figuring out what these tools mean for their teaching, debating their implications, and adjusting their approaches while grappling with ethical dilemmas, institutional policies, and concerns about equity. As one participant put it, “We know that some students are non-native speakers, but what do we do when a foreign language student produces an outstanding essay? Is it right or wrong? How do we mark them?”

AI is not simply a new technological tool; it represents a profound epistemic shift, challenging long-held ideas about knowledge creation, integrity, and pedagogical practices. The rise of tools such as ChatGPT has sparked both excitement and unease. Some educators see AI as an opportunity to enhance learning, while others express deep concerns about its potential to undermine critical thinking and rigor. One participant summed up this tension: “I think AI can actually help develop critical thinking, but again, we must be able to assess that skill separately from the AI, because…it becomes impossible to actually know what the student did and didn’t do.”

This uncertainty is shaping AI adoption. While many educators are actively experimenting with AI, their engagement varies widely. AI is frequently used at the start of the teaching process for idea generation and at the end for reviewing materials. It helps simplify complex concepts, generate quizzes, and proofread content. Yet, despite this curiosity, barriers persist. Time constraints, concerns over rigor, and a lack of institutional guidance make it difficult for educators to fully integrate AI into their practice. “I would like to use AI to plan my assessment structure, but I have no prior experience of using ChatGPT in my teaching so I’m new to this too.”

These inconsistencies are further reflected in concerns about AI’s impact on student learning. Some educators believe AI enhances accessibility and improves student writing, while others fear it encourages passive engagement, oversimplifies content, and reinforces dominant perspectives. The challenge of assessment is particularly pressing. As AI-generated work becomes more sophisticated, traditional assessment models may no longer be fit for purpose.

One participant reflected on this dilemma: “We don’t know what AI can do. How do we know AI can’t generate critical evaluation? We don’t know.”

Beyond pedagogy, AI also raises questions about equity and sustainability. Some educators worry that disparities in access to premium AI tools could exacerbate existing inequalities. “We risk an inclusion issue where some people either have the ability, or the finances to access behind paywalls, and where other people can’t.” one participant commented. Others highlighted AI’s environmental footprint, questioning whether its benefits outweigh its energy demands. These concerns illustrate how the meaning of AI in education is still being shaped – its role is not fixed but negotiated through the ongoing sensemaking of those engaging with it.

What is clear from our findings is that AI is both an opportunity and a challenge, and its future in academia remains uncertain. Moving forward, universities must develop clearer policies to support ethical AI use, redesign assessments to reflect AI’s growing presence, and ensure all students have equal access to these technologies.

Further Reading:

1. Barros, A., Prasad, A., & Śliwa, M. (2023). Generative artificial intelligence and academia: Implication for research, teaching and service. Management Learning, 54(5), 597-604. https://doi.org/10.1177/13505076231201445
2. Butler, N., & Spoelstra, S. (2025). Algorithmic management learning. Management Learning, 56(1), 132-139. https://doi.org/10.1177/13505076241279062
3. Krammer, S. M. (2023). Is there a glitch in the matrix? Artificial intelligence and management education. Management Learning, 0(0). https://doi.org/10.1177/13505076231217667

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